10 papers
MetaStrategy: Generative Ranking with Executable LLM Strategies
Chengyu Lai, Jiuning Lin, Zhibo Xiao +12
Industrial recommender systems rank heterogeneous content under coupled user, business, commercial, and experience objectives. Existing generative ranking methods typically constru…
DREAM Technical Report
Bin Zhang, Bowen Zheng, Chao Yi +74
Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across mo…
RecGPT-Mobile: On-Device Large Language Models for User Intent Understanding in Taobao Feed Recommendation
Bin Zhang, Weipeng Huang, Dimin Wang +9
Predicting a user's next search query from recent interaction behaviors is a critical problem in modern e-commerce systems, particularly in scenarios where user intent evolves rapi…
Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language
Dianqing Lin, Tian Lan, Jiali Zhu +7
While large language models (LLMs) have achieved remarkable success in general language tasks, their performance on Chouxiang Language, a representative subcultural language in the…
CoNRec: Context-Discerning Negative Recommendation with LLMs
Xinda Chen, Jiawei Wu, Yishuang Liu +5
Understanding what users like is relatively straightforward; understanding what users dislike, however, remains a challenging and underexplored problem. Research into users' negati…
Unbiased Platform-Level Causal Estimation for Search Systems: A Competitive Isolation PSM-DID Framework
Ying Song, Yijing Wang, Hui Yang +12
Evaluating platform-level interventions in search-based two-sided marketplaces is fundamentally challenged by systemic effects such as spillovers and network interference. While wi…